Capturing the dynamics of microbial interactions through individual-specific networks
Longitudinal analysis of multivariate individual-specific microbiome profiles over time or across conditions remains dauntin. Most statistical tools and methods that are available to study microbiomes are based on cross-sectional data. Over the past few years, several attempts have been made to mode...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-05-01
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| Series: | Frontiers in Microbiology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmicb.2023.1170391/full |
| _version_ | 1797826832426336256 |
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| author | Behnam Yousefi Behnam Yousefi Behnam Yousefi Federico Melograna Gianluca Galazzo Niels van Best Niels van Best Monique Mommers John Penders John Penders Benno Schwikowski Kristel Van Steen Kristel Van Steen |
| author_facet | Behnam Yousefi Behnam Yousefi Behnam Yousefi Federico Melograna Gianluca Galazzo Niels van Best Niels van Best Monique Mommers John Penders John Penders Benno Schwikowski Kristel Van Steen Kristel Van Steen |
| author_sort | Behnam Yousefi |
| collection | DOAJ |
| description | Longitudinal analysis of multivariate individual-specific microbiome profiles over time or across conditions remains dauntin. Most statistical tools and methods that are available to study microbiomes are based on cross-sectional data. Over the past few years, several attempts have been made to model the dynamics of bacterial species over time or across conditions. However, the field needs novel views on handling microbial interactions in temporal analyses. This study proposes a novel data analysis framework, MNDA, that combines representation learning and individual-specific microbial co-occurrence networks to uncover taxon neighborhood dynamics. As a use case, we consider a cohort of newborns with microbiomes available at 6 and 9 months after birth, and extraneous data available on the mode of delivery and diet changes between the considered time points. Our results show that prediction models for these extraneous outcomes based on an MNDA measure of local neighborhood dynamics for each taxon outperform traditional prediction models solely based on individual-specific microbial abundances. Furthermore, our results show that unsupervised similarity analysis of newborns in the study, again using the notion of a taxon's dynamic neighborhood derived from time-matched individual-specific microbial networks, can reveal different subpopulations of individuals, compared to standard microbiome-based clustering, with potential relevance to clinical practice. This study highlights the complementarity of microbial interactions and abundances in downstream analyses and opens new avenues to personalized prediction or stratified medicine with temporal microbiome data. |
| first_indexed | 2024-04-09T12:38:34Z |
| format | Article |
| id | doaj.art-4f33ab49af4f4d438e350ac234b65a30 |
| institution | Directory Open Access Journal |
| issn | 1664-302X |
| language | English |
| last_indexed | 2024-04-09T12:38:34Z |
| publishDate | 2023-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Microbiology |
| spelling | doaj.art-4f33ab49af4f4d438e350ac234b65a302023-05-15T04:52:27ZengFrontiers Media S.A.Frontiers in Microbiology1664-302X2023-05-011410.3389/fmicb.2023.11703911170391Capturing the dynamics of microbial interactions through individual-specific networksBehnam Yousefi0Behnam Yousefi1Behnam Yousefi2Federico Melograna3Gianluca Galazzo4Niels van Best5Niels van Best6Monique Mommers7John Penders8John Penders9Benno Schwikowski10Kristel Van Steen11Kristel Van Steen12Computational Systems Biomedicine Lab, Institut Pasteur, University Paris City, Paris, FranceÉcole Doctorale Complexite du vivant, Sorbonne University, Paris, FranceBIO3—Laboratory for Systems Medicine, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, BelgiumBIO3—Laboratory for Systems Medicine, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, BelgiumDepartment of Medical Microbiology, Infectious Diseases and Infection Prevention, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, NetherlandsDepartment of Medical Microbiology, Infectious Diseases and Infection Prevention, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, NetherlandsInstitute of Medical Microbiology, Rhine-Westphalia Technical University of Aachen, RWTH University, Aachen, GermanyDepartment of Epidemiology, Care and Public Health Research Institute (CAPHRI), Maastricht University, Maastricht, NetherlandsDepartment of Medical Microbiology, Infectious Diseases and Infection Prevention, School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Center+, Maastricht, NetherlandsDepartment of Medical Microbiology, Infectious Diseases and Infection Prevention, Care and Public Health Research Institute (CAPHRI), Maastricht University Medical Center+, Maastricht, NetherlandsComputational Systems Biomedicine Lab, Institut Pasteur, University Paris City, Paris, FranceBIO3—Laboratory for Systems Medicine, Department of Human Genetics, Katholieke Universiteit Leuven, Leuven, BelgiumBIO3—Laboratory for Systems Genetics, GIGA-R Medical Genomics, University of Lièvzge, Liège, BelgiumLongitudinal analysis of multivariate individual-specific microbiome profiles over time or across conditions remains dauntin. Most statistical tools and methods that are available to study microbiomes are based on cross-sectional data. Over the past few years, several attempts have been made to model the dynamics of bacterial species over time or across conditions. However, the field needs novel views on handling microbial interactions in temporal analyses. This study proposes a novel data analysis framework, MNDA, that combines representation learning and individual-specific microbial co-occurrence networks to uncover taxon neighborhood dynamics. As a use case, we consider a cohort of newborns with microbiomes available at 6 and 9 months after birth, and extraneous data available on the mode of delivery and diet changes between the considered time points. Our results show that prediction models for these extraneous outcomes based on an MNDA measure of local neighborhood dynamics for each taxon outperform traditional prediction models solely based on individual-specific microbial abundances. Furthermore, our results show that unsupervised similarity analysis of newborns in the study, again using the notion of a taxon's dynamic neighborhood derived from time-matched individual-specific microbial networks, can reveal different subpopulations of individuals, compared to standard microbiome-based clustering, with potential relevance to clinical practice. This study highlights the complementarity of microbial interactions and abundances in downstream analyses and opens new avenues to personalized prediction or stratified medicine with temporal microbiome data.https://www.frontiersin.org/articles/10.3389/fmicb.2023.1170391/fullmicrobial neighborhood dynamicslongitudinal microbiome analysisnetwork representation learningindividual-specific networksencoder-decoder neural network |
| spellingShingle | Behnam Yousefi Behnam Yousefi Behnam Yousefi Federico Melograna Gianluca Galazzo Niels van Best Niels van Best Monique Mommers John Penders John Penders Benno Schwikowski Kristel Van Steen Kristel Van Steen Capturing the dynamics of microbial interactions through individual-specific networks Frontiers in Microbiology microbial neighborhood dynamics longitudinal microbiome analysis network representation learning individual-specific networks encoder-decoder neural network |
| title | Capturing the dynamics of microbial interactions through individual-specific networks |
| title_full | Capturing the dynamics of microbial interactions through individual-specific networks |
| title_fullStr | Capturing the dynamics of microbial interactions through individual-specific networks |
| title_full_unstemmed | Capturing the dynamics of microbial interactions through individual-specific networks |
| title_short | Capturing the dynamics of microbial interactions through individual-specific networks |
| title_sort | capturing the dynamics of microbial interactions through individual specific networks |
| topic | microbial neighborhood dynamics longitudinal microbiome analysis network representation learning individual-specific networks encoder-decoder neural network |
| url | https://www.frontiersin.org/articles/10.3389/fmicb.2023.1170391/full |
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